A challenging problem in multi-view stereo (MVS) is accurately estimating dense correspondences across a collection of high-resolution images. Many MVS studies have tried to solve ill-posed MVS problems such as matching ambiguity and high computational complexity. In such problems, learning-based MVS methods [1]}, [2]}, [3]}, [4]}, [5]} usually outperform traditional methods [6]}, [7]}. The learning-based methods are generally composed of three steps, including feature extraction, cost volume formulation, and cost volume regularization. Most studies made an effort to improve the performance of cost formulation [2]}, [9]}, [10]}, [11]} or cost regularization [2]}, [13]}, [14]}. The visibility information [9]}, [10]} or attention techniques [11]}, [13]} are considered for cost aggregation and feature matching, respectively. Furthermore, some studies employed a hybrid 3D UNet structure [2]}, [20]} for cost volume regularization. While these approaches significantly improve MVS performances, their feature extraction networks have some drawbacks that degrade the quality of 3D reconstruction. First, they observed a restricted size of receptive fields from the fixed size of convolutional kernels. This leads to difficulty in learning a robust pixel-level representation when an object’s scale varies extremely in images. As a result, the extracted features cause the low quality of matching costs, especially when the difference of camera poses between reference and source images is large. Second, MVS networks are often trained on low-resolution images because of limited memory and restricted computation time. Therefore, the existing feature extraction methods utilizing fixed-scale feature representation on deep networks could not generalize to high-resolution images in prediction; the MVS performance is downgraded on a high-resolution image set.
